Wire it up. Let it rip.

Multi-agent LLM pipelines that won’t surprise you on the bill.

Define workflows in a small text file. Run them locally with budget caps, parallel branches in git worktrees, and a live TUI. Every run is a git commit you can diff and replay.

Hard cost ceilings. --max-cost 500 --max-tokens 100000 --max-wall-time 30m. Halts mid-run with a snapshot.
Parallel branches in worktrees. Three model variants race in isolated checkouts. No filesystem stomping.
Every run is a git commit. Replay any past run, diff the artifacts, export as a portable bundle.

Install + run GitHub

Three agent backends — native (Anthropic / OpenAI / Gemini / OpenAI-compat), Claude Code (subscription auth, file editing, terminal), and ACP (claude-agent-acp / codex-acp / gemini --acp). Mix per node. · Latest: v0.45.0

Install + run in 60 seconds

# 1. Install — pick one
brew install 2389-research/tap/tracker
# or
go install github.com/2389-research/tracker/cmd/tracker@latest

# 2. Run a built-in workflow (no file needed)
tracker build_product

# 3. Inspect what happened
tracker diagnose

More commands

# Verify your providers + tools
tracker doctor

# Run with the Claude Code agent backend
tracker --backend claude-code build_product

# Run autonomously — LLM judges every gate
tracker --autopilot mid build_product

# Headless — POST gates to your service
tracker --webhook-url https://factory.example.com/gate build_product

# Override workflow params + cap budget
tracker --param model=claude-opus-4-7 \
        --max-cost 500 --max-wall-time 30m build_product

# Drive from any language: tracker --json | jq + parse activity.jsonl
tracker --json build_product

Four flagship workflows are embedded in the binary — no cloning, no examples directory. Or write your own in a .dip file (see below).

How is this different?

Tracker is not a replacement for Temporal or Airflow — those are excellent general workflow schedulers but they don’t model what LLM pipelines actually need (tokens, dollars, parallel branches with filesystem isolation, gates that resume from a webhook). And it’s not a Python library like LangGraph or LangChain — you don’t wire your own observability or eat your own surprise bills.

It’s the local-first, pipelines-as-text-in-git tool you reach for when “just run it” means “run it inside a budget, with a live TUI, and let me diff what happened later.”

What a pipeline looks like

Pipelines are .dip files. Yes, it’s a small DSL — the whole grammar fits on one page; the snippet below is most of what you need.

agent Draft
  model: claude-sonnet-4-6
  prompt: "Draft a brief on ${params.topic}."

human Review
  mode: yes_no

edges
  Draft -> Review
  Review -> Done when ctx.outcome = success

Driving from Python or another language? Run the binary with tracker --json and parse activity.jsonl. A Go library API is available for in-process embedding.

What you get

Kill a runaway agent at $5, not $500.

Hard caps on tokens, cost (cents), and wall-time as CLI flags or workflow defaults. The engine checks between every node and halts with a CostSnapshot on breach.

--max-cost 500 --max-tokens 100000 --max-wall-time 30m

Race three model variants in parallel; pick the winner.

Fan out to concurrent branches in isolated git worktrees, fan in to a join. Built-in pattern for cross-model races without filesystem stomping.

tracker ask_and_execute

See every token, tool call, and gate in a live TUI.

Bubble-tea dashboard, append-only activity log, per-node streams, search, drilldown. Every event also lands in activity.jsonl for grep + post-hoc analysis.

Pause for human approval; resume from a webhook.

Choice, freeform, hybrid, yes/no, and interview gates. Or POST the gate to your service and block on the callback. Per-gate timeout and outbound auth.

Steps pass typed JSON to each other — no glue code.

Declare what an agent produces with writes: [field, field]; downstream steps read those fields as typed values. The runtime validates the output, and a self-healing cascade rescues the common “LLM returned prose around the JSON” case.

`tracker diagnose` tells you which node burned the budget.

Deterministic vs. flaky retries, escalation patterns, stalled loops. Run it after any failure for a triage report. Available as a Go library call too.

Stream pipeline status to your own users.

Every event — node started, gate hit, cost updated, run completed — lands in activity.jsonl on disk and on stdout via tracker --json. Pipe it to your customer dashboard, Slack bot, or status page. Webhook gates POST to your URL on every human-decision point so approvals can render in your own app, not just the TUI.

tracker --json build_product | # pipe into your event bus / SSE relay / dashboard

Four providers, one pipeline

Mix models freely. Route everything through a Cloudflare AI Gateway with --gateway-url for central analytics, caching, and 429 relief.

Anthropic

claude-opus-4-7 · claude-sonnet-4-6 · claude-haiku-4-5

OpenAI

gpt-5.4 · gpt-5.4-mini · gpt-4.1 · o3 · o4-mini

Gemini

gemini-3.1-pro-preview · gemini-2.5-pro · gemini-2.5-flash

OpenAI-Compat

Any OpenAI-compatible endpoint
OpenRouter, vLLM, local proxies

Ready to build?

Install, run a built-in workflow, watch the TUI. If it survives tracker diagnose, write your own pipeline next.

Install + run Explore workflows